Báo cáo hóa học: " Adaptive cognitive media delivery over composite wireless networks" pdf

Farnham EURASIP Journal on Wireless Communications and Networking 2011, 2011:148 http://jwcn.eurasipjournals.com/content/2011/1/148 RESEARCH Open Access Adaptive cognitive media delivery over composite wireless networks Tim Farnham Abstract Over-the-top (OTT) content-on-demand (CoD) media delivery should ideally adapt to the available resources in an opportunistic manner The dynamic nature of the Internet traffic and wireless local area networking technologies, which are typical within the home, must be considered in order to efficiently use resources without the need and limitations associated with centralised or fixed allocation of resources It is also undesirable for devices to continuously monitor the available channels, especially if they are battery powered Therefore, cooperation between devices and modelling of the dynamic adaptive traffic and terminal behaviour is necessary in order that the most suitable resource sharing strategies are employed This article examines the exploitation of cognitive resource management for delivery of OTT CoD within unmanaged wireless environments Channel and traffic models are derived based on the Markov modulated Poisson process and this knowledge is used to derive optimal resource sharing policies Results from simulation and experimental implementation are presented Keywords: cognitive radio, dynamic resource management, streaming, multimedia Introduction The main motivation for applying cognitive resource management to over-the-top (OTT) content-on-demand (CoD) adaptive media delivery is to improve the resource utilisation efficiency, through opportunistic behaviour, without the need and restrictions associated with statically configured or reserved resources for individual users The problem that can occur when OTT CoD is delivered within an unmanaged wireless environment is that unfairness can occur (i.e one user receiving much higher performance than another) Also, there is great potential for under-utilisation of the available resources due to inappropriate reaction to dynamic transient events This is a particular problem associated with adaptive CoD delivery, which continuously adapts itself to the observed performance, especially when there are several radio resource options available that can dynamically be selected Previous research in the field of cognitive radio (CR) resource management has considered opportunistic resource sharing (such as within [1-4]) However, application of these techniques to adaptive OTT CoD media delivery introduces different problems related to the adaptive nature of the application Correspondence: tim.farnham@toshiba-trel.com Toshiba Research Europe Ltd., 32 Queen Square, Bristol BS1 4ND, UK traffic and associated fairness considerations, outlined in [5], which will interact with the dynamic channel state estimation and modelling approach We therefore focus on the evaluation of a CR resource management approach applied to wireless unmanaged OTT CoD services Standardisation activities associated with CR solutions, for composite networks, focus on the architecture, information models and policies necessary to deploy distributed decision making in a flexible manner These standards are key enablers of the vision to facilitate advanced radio resource management using a common information model (as introduced in [6]) For instance, the IEEE 1900.4 (2009) standard specifications (see [7,8]) provide the system and functional architectures and the information model (including policies) necessary to split cognitive decision-making processes between network and terminal entities The standard allows for policies and context information, governing decision making, to be distributed to client terminal devices to assist the decision of how to exploit various access options, within the constraints imposed by the policies Within the framework of this standard, the exemplary steps involved in a typical distributed radio resource utilisation optimisation use-case are collect context © 2011 Farnham; licensee Springer This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited Farnham EURASIP Journal on Wireless Communications and Networking 2011, 2011:148 http://jwcn.eurasipjournals.com/content/2011/1/148 information, generate radio resource selection policies and perform reconfiguration on the terminal side (within the constraints of these policies) The context corresponds to either the terminal side (such as the observed channel and link measurements, etc.) or the radio access network side (such as cell coverage area and associated measurements) Within previous research (such as [9,10]), the useful context measurements are packet delay, packet loss rate, signal-to-noise ratio and channel activity/load The policies are then derived to specify the conditions placed on the radio resource selection process For instance, instructing the terminals to use certain radio access networks or channel configurations only when the specified conditions match In this article, we first examine the use of this type of distributed decision making (enabled by the IEEE 1900.4 framework) to improve the performance and efficiency of CoD media delivery within a wireless context and then consider derivation (refinement) of policies to improve the decision-making strategies to provide better overall performance In order to quantify the benefit and optimality of the policies, which are discussed in Section 4, it is necessary to consider the traffic loading and media delivery performance goals that are introduced in Sections and Performance is evaluated by both simulation models (Section 5) and experimental test-bed implementation (Section 6) to consider a real application deployment scenario Final conclusions are drawn on the merits of this approach to CoD media delivery over composite wireless networks Channel model 2.1 Dynamic channel model Understanding the dynamic nature of channel load (such as from the Internet traffic), using a representation of channel state, is an important way of determining optimal resource utilisation strategies in unmanaged scenarios We utilise a channel model based on the Markov modulated Poisson process (MMPP) approach (described in [11]) to assist the decision-making process In this model, the mean channel rate switches between two or more values (e.g lx, and l x , ), with certain probabilities (p 1-2 and p 2-1) In this manner when the overall time period of interest is large (compared with the transmission time) and so p2-1 < < and p1-2 < < 1, the time spent in each state is proportional to these probabilities and the overall rate becomes; (p2-1 lx, + p1-2 lx, )/(p1-2 + p2-1) This general type of model is therefore applicable for composite network scenarios with Internet traffic The above analysis indicates the goal to use channels that are likely to exhibit the highest rate and will remain in the high rate state for sufficient time for transfer of media chunks (bursts) in the target time NT The Page of 12 consequence of wrongly estimating the channel state depends on the relative difference between mean rates (i.e lx, - lx, ) and the dynamics (probability) of state transition 2.2 Channel selection The above analysis assumes knowledge of the channel rates and rate states in order to optimally select channels However, continuous measurement (such as channel probing and activity monitoring) is not desirable due to the implied need to use wideband and inefficient spectrum sensing devices or to continuously switch and probe different channels Therefore, it is desirable to restrict the monitoring time to reasonable periodic intervals and use generic channel activity/load context distribution (i.e such as using IEEE1900.4 context data see [8]) Within the IEEE 1900.4 framework, the observed channel measurement class (managed object) supports the ability for terminals to monitor the observed channel activity/load in a generic manner In a similar way the link measurement managed object permits link related measurements (such as link signal level, error rate and latency etc.) These context measurements can be distributed (i.e shared) via the network reconfiguration manager with other terminals This is abstracted using a generic data model to facilitate the distribution of context data in the most appropriate and timely manner For instance, context can be sent only when certain value thresholds are crossed or using a particular periodic sampling (i.e averaging) Using such techniques, the amount of context data distribution is reduced and is also made more meaningful and useful for use within the radio resource usage optimisation policies This also prevents the terminals from having to continuously monitor all the available channels all of the time, which would incur excessive complexity and power consumption In addition to the passive channel observation/measurement, it is also possible to use the active measurements (such as observing transmission latency) to determine if the channel rate state has changed during active transmission, without incurring additional channel monitoring/measurement or distribution overhead, for instance, as part of the criteria to trigger channel switching Therefore, we make a specific interpretation of the definition for observed channel measurement context, for the purposes of this study, as shown in Table 1, which contains a passive channel context measurement (that is activity/load) and is distributed using IEEE 1900.4 approach, and an active channel context measurement that is computed locally and not distributed We intentionally omit other typical channel and link context data related to signal strength and error rate as we focus on stationary scenarios with no terminal mobility However, these would be Farnham EURASIP Journal on Wireless Communications and Networking 2011, 2011:148 http://jwcn.eurasipjournals.com/content/2011/1/148 Page of 12 Table Context measurement attribute definitions Attribute Definition Units Passive context Activity/load Total observed transmitted bytes over a specified time window (from all transmitters using the channel) divided by the time window duration Mbps Active context Latency The average one-way delay for transferring a packet within a media “chunk” over the corresponding channel after the chunk is presented for transmission ms Alternatively, the average time taken for the delivery of all packets within a complete media “chunk” over the corresponding channel applicable in other deployment scenarios and have been subject of other studies (such as in [10]) The above rationale for efficient channel monitoring assumes that the periodic channel context can be combined to derive better resource selection decision policies The goal of the optimal channel selection is therefore to avoid channel congestion (i.e a low rate state) by aiming to always select the channel(s) with the least load/activity and also the lowest latency To achieve this, the active latency performance observation considers the media “chunk” delivery (rather than passive observation or active probing) If the observed latency is greater than a certain threshold (tHigh) then it is an indication that the channel is in a low rate state and another channel should be used Similarly, if the observed latency is below a certain threshold (tLow) it is indicative that the channel is in a high rate state and should be used more (i.e it is underutilised) In a similar manner, if the channel activity context value is above a certain threshold (chacHigh) it also indicates that the channel is in a low rate state and likewise if the activity is below a threshold (chacLow) then the channel is in a high rate state To solve the problem of finding the optimal channel selection strategy, based on the combination of both passive and active measurement threshold criteria, we also need to consider the adaptive nature of the OTT CoD application traffic, which makes it harder to determine optimal thresholds Application traffic Media services delivered using CoD have a special property, which is the ability to retrieve the required content chunks a certain time ahead of the need for playback of the content at the terminal Typically, users are prepared to wait for an initial period of time during the initialisation of a content stream, although this is only in the order of seconds and ideally is on a sub-second scale Adaptive streaming approaches are most applicable for OTT CoD delivery in dynamic channels, as they adapt to the available channel mean rate for chunk delivery (i e lx), so that the application can strive to achieve the highest quality level (QL)/rate (n) supported by the channel, and hence the best possible quality The timescale for the adaptation is normally per media chunk, and is in the order of seconds (e.g s), so as to avoid reacting to very dynamic transient effects Therefore, channel selection policies that use both proactive and reactive selection for channels are desirable Exploiting channel knowledge for adaptive streaming delivery services requires a means to measure adaptive streaming performance, which is discussed next 3.1 Adaptive streaming Adaptive streaming-based CoD assumes that content is encoded into several QLs that correspond to different average rates (n), with higher rate equating to better quality The measure of performance that we use is based on the QLs of the successfully delivered content chunks The typical adaptive behaviour is for an initial estimate of the maximum and minimum QL to be determined during the content initialisation phase The client requests the manifest file for the content item, which includes the available QLs, and also estimates the channel rate and screen resolution to determine the appropriate bounds Then the client starts with the worst quality (or often a mid-range quality) and requests content chunks gradually increasing quality until either the maximum bound is reached or the channel rate is reached In order to take into account the level of user satisfaction obtained when watching adaptive streamed video we define each QL to have an incremental dissatisfaction multiplier (i) corresponding to the QL index In this way, the higher the QL index (implying lower quality) the greater is the user dissatisfaction The resulting expression for user dissatisfaction (1) is derived based on a mirror representation of the standard mean opinion scores (MOS) that have been measured for adaptive streaming applications by subjective testing For instance, the standard five level MOS equates to the du by the expression du = c(5 - MOS), where c is a constant that depends on the number of encoded QLs of the media source using typical MPEG4 adaptive streaming content Therefore, the overall level of dissatisfaction observed by user (u) is not a linear relationship with Farnham EURASIP Journal on Wireless Communications and Networking 2011, 2011:148 http://jwcn.eurasipjournals.com/content/2011/1/148 QL, but instead is given by the d u expression that is defined in (1) and implies that, for instance, observing a QL index of for 10% of the time (and for the rest) results in a dissatisfaction of 1.5, which is actually perceived to be much worse than if a QL index of was observed 100% of the time (i.e resulting in a dissatisfaction of 1) N du = i.Pu,i (QL ≥ i) (1) i=1 where Pu, i (QL ≥ i) is the proportion of the time (or chunks) that the observed QL index for user u is greater or equal to the ith index In order to provide a combined dissatisfaction level for all users we take each du and weight it with the corresponding user privilege level Wu before summation to arrive at a combined overall dissatisfaction (D), for all users, as defined in (2) The privilege level is a way to take into account that some users may be more important and should have a lower dissatisfaction level than other users M D= du Wu (2) u=1 The aim is now to minimise the overall user dissatisfaction (D), which is observed In order to achieve this aim it is necessary to carefully consider the timescales over which estimates are made For instance, as in all adaptive systems that vary dynamically, taking a period of time that is too short will result in variable and inaccurate predictions of P u, i (QL ≥ i), that may lead to incorrect decisions being taken Resource management policies In this section, we consider the impact of the CoD rate adaptation policies that govern the behaviour of the application as well as channel selection For this we must first consider what criteria or constraints the policies utilise and how they are formed 4.1 General form The aim of policy-based approaches for resource management (such as within IEEE 1900.4 [8]) is to decouple the policy derivation and evaluation process from the policy enforcement point In this manner, it becomes possible to devolve decision-making functions from one logical entity (server) to another (client terminals) The policy rules considered are a subset of the general Event-Condition-Action (ECA) form It is simplified by the fact that all policy rules are evaluated in a priority order on occurrence of a corresponding event (causing an attribute update) The conditions within a policy rule Page of 12 are formed from simple threshold criteria corresponding to different device specific attributes, and actions are only of three possible types as shown below: IF { } {} THEN where is attribute, operator and threshold criteria and is either EXCLUDE/MUSTUSE/ MAYUSE and is OR/AND The meaning of the action EXCLUDE is that the objects (such as channels and links) matching the condition criteria must not be selected The action MUSTUSE implies that the matching objects must be used in preference to objects (i.e channels or links) that are not matching the policy rule criteria The additional action MAYUSE is the default action when neither of the EXCLUDE or MUSTUSE condition applies to them and so there is no constraint on whether or not the associated object is used However, it can also be used by a high priority rule to specify that a low importance be placed on certain options Each policy rule within a policy set (ordered list of rules) is then evaluated in a priority order with the first matching criteria taking precedence over subsequent rules In this manner, the evaluation of the policies results in an unambiguous association of the objects (i.e channels or links) with the action EXCLUDE, MUSTUSE or MAYUSE The policy does not specify how the final selection is performed but objects tagged with EXCLUDE cannot be selected under any circumstance and objects associated with the MUSTUSE action take precedence over the objects tagged with the default MAYUSE action 4.2 Reactive policies We consider reactive policies to be those that trigger on changes in observed active context performance measurements For instance, the average latency of the packets delivered can form the basis for one reactive threshold Two latency thresholds are assumed to be useful for OTT CoD adaptive streaming, which are a high latency threshold, tHigh, and a low latency threshold, tLow The reactive policies that can be derived to trigger a channel reselection based on the observed latency, such as IF {channel.latency(u) >tHigh } THEN EXCLUDE IF {channel.latency(u) chacHigh1} OR{channel.latency(2) >tHigh2} OR {channel.activityLevel(2) >chacHigh2} THEN EXCLUDE IF {channel.latency(1)